Reinforcement learning based adaptive handover in ultra-dense cellular networks with small cells
The dense deployment of the small base station (BS) in fifth-generation commination system can satisfy the user demand on high data rate transmission. On the other hand, such a scenario also increases the complexity of mobility management. In this paper, we developed a Q-learning framework exploit...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/64054/ |
| _version_ | 1848800084752334848 |
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| author | LIU, Qianyu Kwong, Chiew Foong Sun, Wei Li, Lincan Zhao, Haoyu |
| author_facet | LIU, Qianyu Kwong, Chiew Foong Sun, Wei Li, Lincan Zhao, Haoyu |
| author_sort | LIU, Qianyu |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The dense deployment of the small base station (BS) in fifth-generation commination system can satisfy the user demand
on high data rate transmission. On the other hand, such a scenario also increases the complexity of mobility management. In this paper, we developed a Q-learning framework exploiting user radio condition, that is, reference signal receiving power (RSRP), signal to inference and noise ratio (SINR) and transmission distance to learn the optimal policy for handover triggering. The objective of the proposed approach is to increase the mobility robustness of user in ultra-dense networks (UDNs) by minimizing redundant handover and handover failure ratio. Simulation results show that our proposed triggering mechanism efficiency suppresses ping-pong handover effect while maintaining handover failure at an acceptable level. Besides, the proposed triggering mechanism can trigger the handover process directly without HOM and TTT. The respond speed of triggering mechanism can thus be increased. |
| first_indexed | 2025-11-14T20:45:56Z |
| format | Conference or Workshop Item |
| id | nottingham-64054 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:45:56Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-640542020-12-21T05:57:36Z https://eprints.nottingham.ac.uk/64054/ Reinforcement learning based adaptive handover in ultra-dense cellular networks with small cells LIU, Qianyu Kwong, Chiew Foong Sun, Wei Li, Lincan Zhao, Haoyu The dense deployment of the small base station (BS) in fifth-generation commination system can satisfy the user demand on high data rate transmission. On the other hand, such a scenario also increases the complexity of mobility management. In this paper, we developed a Q-learning framework exploiting user radio condition, that is, reference signal receiving power (RSRP), signal to inference and noise ratio (SINR) and transmission distance to learn the optimal policy for handover triggering. The objective of the proposed approach is to increase the mobility robustness of user in ultra-dense networks (UDNs) by minimizing redundant handover and handover failure ratio. Simulation results show that our proposed triggering mechanism efficiency suppresses ping-pong handover effect while maintaining handover failure at an acceptable level. Besides, the proposed triggering mechanism can trigger the handover process directly without HOM and TTT. The respond speed of triggering mechanism can thus be increased. 2020-10-12 Conference or Workshop Item PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/64054/1/Reinforcement%20Learning%20based%20Adaptive%20Handover....pdf LIU, Qianyu, Kwong, Chiew Foong, Sun, Wei, Li, Lincan and Zhao, Haoyu (2020) Reinforcement learning based adaptive handover in ultra-dense cellular networks with small cells. In: International Symposium on Artificial Intelligence and Robotics 2020, 8 August 2020-10 August 2020, Kitakyushu，Japan. handover; reinforcement learning; ultra-dense networks http://dx.doi.org/10.1117/12.2580119 10.1117/12.2580119 10.1117/12.2580119 10.1117/12.2580119 |
| spellingShingle | handover; reinforcement learning; ultra-dense networks LIU, Qianyu Kwong, Chiew Foong Sun, Wei Li, Lincan Zhao, Haoyu Reinforcement learning based adaptive handover in ultra-dense cellular networks with small cells |
| title | Reinforcement learning based adaptive handover in ultra-dense cellular networks with small cells |
| title_full | Reinforcement learning based adaptive handover in ultra-dense cellular networks with small cells |
| title_fullStr | Reinforcement learning based adaptive handover in ultra-dense cellular networks with small cells |
| title_full_unstemmed | Reinforcement learning based adaptive handover in ultra-dense cellular networks with small cells |
| title_short | Reinforcement learning based adaptive handover in ultra-dense cellular networks with small cells |
| title_sort | reinforcement learning based adaptive handover in ultra-dense cellular networks with small cells |
| topic | handover; reinforcement learning; ultra-dense networks |
| url | https://eprints.nottingham.ac.uk/64054/ https://eprints.nottingham.ac.uk/64054/ https://eprints.nottingham.ac.uk/64054/ |